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This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antibiotic results are from our example_isolates data set. All patient names were created using online surname generators and are only in place for practice purposes.




A tibble with 500 observations and 53 variables:

  • Identification number
    ID of the sample

  • Specimen number
    ID of the specimen

  • Organism
    Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using

  • Country
    Country of origin

  • Laboratory
    Name of laboratory

  • Last name
    Fictitious last name of patient

  • First name
    Fictitious initial of patient

  • Sex
    Fictitious gender of patient

  • Age
    Fictitious age of patient

  • Age category
    Age group, can also be looked up using age_groups()

  • Date of admission
    Date of hospital admission

  • Specimen date
    Date when specimen was received at laboratory

  • Specimen type
    Specimen type or group

  • Specimen type (Numeric)
    Translation of "Specimen type"

  • Reason
    Reason of request with Differential Diagnosis

  • Isolate number
    ID of isolate

  • Organism type
    Type of microorganism, can also be looked up using mo_type()

  • Serotype
    Serotype of microorganism

  • Beta-lactamase
    Microorganism produces beta-lactamase?

  • ESBL
    Microorganism produces extended spectrum beta-lactamase?

  • Carbapenemase
    Microorganism produces carbapenemase?

  • MRSA screening test
    Microorganism is possible MRSA?

  • Inducible clindamycin resistance
    Clindamycin can be induced?

  • Comment
    Other comments

  • Date of data entry
    Date this data was entered in WHONET

    28 different antibiotics. You can lookup the abbreviations in the antibiotics data set, or use e.g. ab_name("AMP") to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using as.sir().


Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit our website for the download links. The actual files are of course available on our GitHub repository.


#> # A tibble: 500 × 53
#>    `Identification number` `Specimen number` Organism Country         Laboratory
#>    <chr>                               <int> <chr>    <chr>           <chr>     
#>  1 fe41d7bafa                           1748 SPN      Belgium         National …
#>  2 91f175ec37                           1767 eco      The Netherlands National …
#>  3 cc4015056e                           1343 eco      The Netherlands National …
#>  4 e864b692f5                           1894 MAP      Denmark         National …
#>  5 3d051fe345                           1739 PVU      Belgium         National …
#>  6 c80762a08d                           1846 103      The Netherlands National …
#>  7 8022d3727c                           1628 103      Denmark         National …
#>  8 f3dc5f553d                           1493 eco      The Netherlands National …
#>  9 15add38f6c                           1847 eco      France          National …
#> 10 fd41248def                           1458 eco      Germany         National …
#> # ℹ 490 more rows
#> # ℹ 48 more variables: `Last name` <chr>, `First name` <chr>, Sex <chr>,
#> #   Age <dbl>, `Age category` <chr>, `Date of admission` <date>,
#> #   `Specimen date` <date>, `Specimen type` <chr>,
#> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>,
#> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>,
#> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, …